Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations750000
Missing cells233124
Missing cells (%)2.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory281.7 MiB
Average record size in memory393.8 B

Variable types

Numeric6
Categorical5
Text1

Alerts

Episode_Length_minutes is highly overall correlated with Listening_Time_minutesHigh correlation
Genre is highly overall correlated with Podcast_NameHigh correlation
Listening_Time_minutes is highly overall correlated with Episode_Length_minutesHigh correlation
Podcast_Name is highly overall correlated with GenreHigh correlation
Episode_Length_minutes has 87093 (11.6%) missing values Missing
Guest_Popularity_percentage has 146030 (19.5%) missing values Missing
id is uniformly distributed Uniform
id has unique values Unique
Number_of_Ads has 217592 (29.0%) zeros Zeros
Listening_Time_minutes has 8551 (1.1%) zeros Zeros

Reproduction

Analysis started2025-04-24 10:07:15.124516
Analysis finished2025-04-24 10:07:32.313932
Duration17.19 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

Uniform  Unique 

Distinct750000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean374999.5
Minimum0
Maximum749999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2025-04-24T19:07:32.450211image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37499.95
Q1187499.75
median374999.5
Q3562499.25
95-th percentile712499.05
Maximum749999
Range749999
Interquartile range (IQR)374999.5

Descriptive statistics

Standard deviation216506.5
Coefficient of variation (CV)0.57735142
Kurtosis-1.2
Mean374999.5
Median Absolute Deviation (MAD)187500
Skewness-1.9631002 × 10-15
Sum2.8124962 × 1011
Variance4.6875062 × 1010
MonotonicityStrictly increasing
2025-04-24T19:07:32.544222image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
499993 1
 
< 0.1%
499995 1
 
< 0.1%
499996 1
 
< 0.1%
499997 1
 
< 0.1%
499998 1
 
< 0.1%
499999 1
 
< 0.1%
500000 1
 
< 0.1%
500001 1
 
< 0.1%
500002 1
 
< 0.1%
Other values (749990) 749990
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
749999 1
< 0.1%
749998 1
< 0.1%
749997 1
< 0.1%
749996 1
< 0.1%
749995 1
< 0.1%
749994 1
< 0.1%
749993 1
< 0.1%
749992 1
< 0.1%
749991 1
< 0.1%
749990 1
< 0.1%

Podcast_Name
Categorical

High correlation 

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.2 MiB
Tech Talks
 
22847
Sports Weekly
 
20053
Funny Folks
 
19635
Tech Trends
 
19549
Fitness First
 
19488
Other values (43)
648428 

Length

Max length19
Median length17
Mean length12.761455
Min length8

Characters and Unicode

Total characters9571091
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMystery Matters
2nd rowJoke Junction
3rd rowStudy Sessions
4th rowDigital Digest
5th rowMind & Body

Common Values

ValueCountFrequency (%)
Tech Talks 22847
 
3.0%
Sports Weekly 20053
 
2.7%
Funny Folks 19635
 
2.6%
Tech Trends 19549
 
2.6%
Fitness First 19488
 
2.6%
Business Insights 19480
 
2.6%
Style Guide 19364
 
2.6%
Game Day 19272
 
2.6%
Melody Mix 18889
 
2.5%
Criminal Minds 17735
 
2.4%
Other values (38) 553688
73.8%

Length

2025-04-24T19:07:32.639588image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tech 42396
 
2.8%
matters 42034
 
2.7%
business 36492
 
2.4%
sports 36244
 
2.4%
crime 33747
 
2.2%
digest 29562
 
1.9%
28325
 
1.8%
living 26884
 
1.8%
talks 22847
 
1.5%
news 22818
 
1.5%
Other values (78) 1210436
79.0%

Most occurring characters

ValueCountFrequency (%)
e 1008397
 
10.5%
s 785605
 
8.2%
781785
 
8.2%
i 653926
 
6.8%
t 576726
 
6.0%
n 574700
 
6.0%
r 527739
 
5.5%
o 477348
 
5.0%
a 476114
 
5.0%
l 322554
 
3.4%
Other values (33) 3386197
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9571091
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1008397
 
10.5%
s 785605
 
8.2%
781785
 
8.2%
i 653926
 
6.8%
t 576726
 
6.0%
n 574700
 
6.0%
r 527739
 
5.5%
o 477348
 
5.0%
a 476114
 
5.0%
l 322554
 
3.4%
Other values (33) 3386197
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9571091
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1008397
 
10.5%
s 785605
 
8.2%
781785
 
8.2%
i 653926
 
6.8%
t 576726
 
6.0%
n 574700
 
6.0%
r 527739
 
5.5%
o 477348
 
5.0%
a 476114
 
5.0%
l 322554
 
3.4%
Other values (33) 3386197
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9571091
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1008397
 
10.5%
s 785605
 
8.2%
781785
 
8.2%
i 653926
 
6.8%
t 576726
 
6.0%
n 574700
 
6.0%
r 527739
 
5.5%
o 477348
 
5.0%
a 476114
 
5.0%
l 322554
 
3.4%
Other values (33) 3386197
35.4%
Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.1 MiB
2025-04-24T19:07:32.780747image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length11
Median length10
Mean length9.9295733
Min length9

Characters and Unicode

Total characters7447180
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEpisode 98
2nd rowEpisode 26
3rd rowEpisode 16
4th rowEpisode 45
5th rowEpisode 86
ValueCountFrequency (%)
episode 750000
50.0%
71 10515
 
0.7%
62 10373
 
0.7%
31 10292
 
0.7%
61 9991
 
0.7%
69 9864
 
0.7%
23 9762
 
0.7%
63 9743
 
0.6%
81 9741
 
0.6%
64 9686
 
0.6%
Other values (91) 660033
44.0%
2025-04-24T19:07:33.070542image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 750000
10.1%
i 750000
10.1%
s 750000
10.1%
o 750000
10.1%
d 750000
10.1%
e 750000
10.1%
750000
10.1%
p 750000
10.1%
6 160174
 
2.2%
3 158352
 
2.1%
Other values (8) 1128654
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7447180
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 750000
10.1%
i 750000
10.1%
s 750000
10.1%
o 750000
10.1%
d 750000
10.1%
e 750000
10.1%
750000
10.1%
p 750000
10.1%
6 160174
 
2.2%
3 158352
 
2.1%
Other values (8) 1128654
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7447180
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 750000
10.1%
i 750000
10.1%
s 750000
10.1%
o 750000
10.1%
d 750000
10.1%
e 750000
10.1%
750000
10.1%
p 750000
10.1%
6 160174
 
2.2%
3 158352
 
2.1%
Other values (8) 1128654
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7447180
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 750000
10.1%
i 750000
10.1%
s 750000
10.1%
o 750000
10.1%
d 750000
10.1%
e 750000
10.1%
750000
10.1%
p 750000
10.1%
6 160174
 
2.2%
3 158352
 
2.1%
Other values (8) 1128654
15.2%

Episode_Length_minutes
Real number (ℝ)

High correlation  Missing 

Distinct12268
Distinct (%)1.9%
Missing87093
Missing (%)11.6%
Infinite0
Infinite (%)0.0%
Mean64.504738
Minimum0
Maximum325.24
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2025-04-24T19:07:33.171288image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.68
Q135.73
median63.84
Q394.07
95-th percentile115.29
Maximum325.24
Range325.24
Interquartile range (IQR)58.34

Descriptive statistics

Standard deviation32.969603
Coefficient of variation (CV)0.51111909
Kurtosis-1.2030327
Mean64.504738
Median Absolute Deviation (MAD)29.16
Skewness-0.0020056126
Sum42760643
Variance1086.9947
MonotonicityNot monotonic
2025-04-24T19:07:33.258767image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.6 925
 
0.1%
34.4 617
 
0.1%
30.69 576
 
0.1%
31.68 533
 
0.1%
31.46 491
 
0.1%
47.02 461
 
0.1%
29.61 448
 
0.1%
106.52 426
 
0.1%
111.68 420
 
0.1%
114.98 411
 
0.1%
Other values (12258) 657599
87.7%
(Missing) 87093
 
11.6%
ValueCountFrequency (%)
0 1
 
< 0.1%
1.24 1
 
< 0.1%
1.48 1
 
< 0.1%
1.84 1
 
< 0.1%
2.47 4
 
< 0.1%
2.97 1
 
< 0.1%
5 38
< 0.1%
5.0000636 1
 
< 0.1%
5.00006409 6
 
< 0.1%
5.00006607 1
 
< 0.1%
ValueCountFrequency (%)
325.24 1
 
< 0.1%
120.93 1
 
< 0.1%
120.73 1
 
< 0.1%
120.64 2
 
< 0.1%
120.37 2
 
< 0.1%
120.32 1
 
< 0.1%
120.06 1
 
< 0.1%
119.99 7
 
< 0.1%
119.98 55
< 0.1%
119.97 44
< 0.1%

Genre
Categorical

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.3 MiB
Sports
87606 
Technology
86256 
True Crime
85059 
Lifestyle
82461 
Comedy
81453 
Other values (5)
327165 

Length

Max length10
Median length9
Mean length7.4019627
Min length4

Characters and Unicode

Total characters5551472
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue Crime
2nd rowComedy
3rd rowEducation
4th rowTechnology
5th rowHealth

Common Values

ValueCountFrequency (%)
Sports 87606
11.7%
Technology 86256
11.5%
True Crime 85059
11.3%
Lifestyle 82461
11.0%
Comedy 81453
10.9%
Business 80521
10.7%
Health 71416
9.5%
News 63385
8.5%
Music 62743
8.4%
Education 49100
6.5%

Length

2025-04-24T19:07:33.342405image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T19:07:33.438751image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
sports 87606
10.5%
technology 86256
10.3%
true 85059
10.2%
crime 85059
10.2%
lifestyle 82461
9.9%
comedy 81453
9.8%
business 80521
9.6%
health 71416
8.6%
news 63385
7.6%
music 62743
7.5%

Most occurring characters

ValueCountFrequency (%)
e 718071
 
12.9%
s 537758
 
9.7%
o 390671
 
7.0%
i 359884
 
6.5%
t 290583
 
5.2%
u 277423
 
5.0%
r 257724
 
4.6%
y 250170
 
4.5%
l 240133
 
4.3%
n 215877
 
3.9%
Other values (19) 2013178
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5551472
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 718071
 
12.9%
s 537758
 
9.7%
o 390671
 
7.0%
i 359884
 
6.5%
t 290583
 
5.2%
u 277423
 
5.0%
r 257724
 
4.6%
y 250170
 
4.5%
l 240133
 
4.3%
n 215877
 
3.9%
Other values (19) 2013178
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5551472
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 718071
 
12.9%
s 537758
 
9.7%
o 390671
 
7.0%
i 359884
 
6.5%
t 290583
 
5.2%
u 277423
 
5.0%
r 257724
 
4.6%
y 250170
 
4.5%
l 240133
 
4.3%
n 215877
 
3.9%
Other values (19) 2013178
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5551472
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 718071
 
12.9%
s 537758
 
9.7%
o 390671
 
7.0%
i 359884
 
6.5%
t 290583
 
5.2%
u 277423
 
5.0%
r 257724
 
4.6%
y 250170
 
4.5%
l 240133
 
4.3%
n 215877
 
3.9%
Other values (19) 2013178
36.3%
Distinct8038
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.859901
Minimum1.3
Maximum119.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2025-04-24T19:07:33.547456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile24.79
Q139.41
median60.05
Q379.53
95-th percentile95.77
Maximum119.46
Range118.16
Interquartile range (IQR)40.12

Descriptive statistics

Standard deviation22.873098
Coefficient of variation (CV)0.38211052
Kurtosis-1.2067021
Mean59.859901
Median Absolute Deviation (MAD)20.04
Skewness0.0049262753
Sum44894926
Variance523.17859
MonotonicityNot monotonic
2025-04-24T19:07:33.637774image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.68 560
 
0.1%
26.72 523
 
0.1%
56.29 490
 
0.1%
30.14 445
 
0.1%
31.57 439
 
0.1%
58.71 431
 
0.1%
80.43 428
 
0.1%
67.54 411
 
0.1%
36.79 410
 
0.1%
67.19 401
 
0.1%
Other values (8028) 745462
99.4%
ValueCountFrequency (%)
1.3 1
 
< 0.1%
1.47 1
 
< 0.1%
1.73 1
 
< 0.1%
1.77 2
 
< 0.1%
1.89 2
 
< 0.1%
2.95 2
 
< 0.1%
20 18
 
< 0.1%
20.01 69
< 0.1%
20.02 42
< 0.1%
20.03 62
< 0.1%
ValueCountFrequency (%)
119.46 1
 
< 0.1%
118.93 1
 
< 0.1%
118.73 1
 
< 0.1%
118.69 1
 
< 0.1%
117.76 2
 
< 0.1%
117.14 5
< 0.1%
115.18 1
 
< 0.1%
114.97 1
 
< 0.1%
114.73 1
 
< 0.1%
112.44 1
 
< 0.1%

Publication_Day
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.1 MiB
Sunday
115946 
Monday
111963 
Friday
108237 
Wednesday
107886 
Thursday
104360 
Other values (2)
201608 

Length

Max length9
Median length8
Mean length7.1166547
Min length6

Characters and Unicode

Total characters5337491
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThursday
2nd rowSaturday
3rd rowTuesday
4th rowMonday
5th rowMonday

Common Values

ValueCountFrequency (%)
Sunday 115946
15.5%
Monday 111963
14.9%
Friday 108237
14.4%
Wednesday 107886
14.4%
Thursday 104360
13.9%
Saturday 103505
13.8%
Tuesday 98103
13.1%

Length

2025-04-24T19:07:33.728739image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T19:07:33.810702image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
sunday 115946
15.5%
monday 111963
14.9%
friday 108237
14.4%
wednesday 107886
14.4%
thursday 104360
13.9%
saturday 103505
13.8%
tuesday 98103
13.1%

Most occurring characters

ValueCountFrequency (%)
d 857886
16.1%
a 853505
16.0%
y 750000
14.1%
u 421914
7.9%
n 335795
 
6.3%
r 316102
 
5.9%
e 313875
 
5.9%
s 310349
 
5.8%
S 219451
 
4.1%
T 202463
 
3.8%
Other values (7) 756151
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5337491
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 857886
16.1%
a 853505
16.0%
y 750000
14.1%
u 421914
7.9%
n 335795
 
6.3%
r 316102
 
5.9%
e 313875
 
5.9%
s 310349
 
5.8%
S 219451
 
4.1%
T 202463
 
3.8%
Other values (7) 756151
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5337491
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 857886
16.1%
a 853505
16.0%
y 750000
14.1%
u 421914
7.9%
n 335795
 
6.3%
r 316102
 
5.9%
e 313875
 
5.9%
s 310349
 
5.8%
S 219451
 
4.1%
T 202463
 
3.8%
Other values (7) 756151
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5337491
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 857886
16.1%
a 853505
16.0%
y 750000
14.1%
u 421914
7.9%
n 335795
 
6.3%
r 316102
 
5.9%
e 313875
 
5.9%
s 310349
 
5.8%
S 219451
 
4.1%
T 202463
 
3.8%
Other values (7) 756151
14.2%

Publication_Time
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.0 MiB
Night
196849 
Evening
195778 
Afternoon
179460 
Morning
177913 

Length

Max length9
Median length7
Mean length6.9536293
Min length5

Characters and Unicode

Total characters5215222
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight
2nd rowAfternoon
3rd rowEvening
4th rowMorning
5th rowAfternoon

Common Values

ValueCountFrequency (%)
Night 196849
26.2%
Evening 195778
26.1%
Afternoon 179460
23.9%
Morning 177913
23.7%

Length

2025-04-24T19:07:33.919302image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T19:07:33.994826image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
night 196849
26.2%
evening 195778
26.1%
afternoon 179460
23.9%
morning 177913
23.7%

Most occurring characters

ValueCountFrequency (%)
n 1106302
21.2%
i 570540
10.9%
g 570540
10.9%
o 536833
10.3%
t 376309
 
7.2%
e 375238
 
7.2%
r 357373
 
6.9%
N 196849
 
3.8%
h 196849
 
3.8%
E 195778
 
3.8%
Other values (4) 732611
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5215222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1106302
21.2%
i 570540
10.9%
g 570540
10.9%
o 536833
10.3%
t 376309
 
7.2%
e 375238
 
7.2%
r 357373
 
6.9%
N 196849
 
3.8%
h 196849
 
3.8%
E 195778
 
3.8%
Other values (4) 732611
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5215222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1106302
21.2%
i 570540
10.9%
g 570540
10.9%
o 536833
10.3%
t 376309
 
7.2%
e 375238
 
7.2%
r 357373
 
6.9%
N 196849
 
3.8%
h 196849
 
3.8%
E 195778
 
3.8%
Other values (4) 732611
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5215222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1106302
21.2%
i 570540
10.9%
g 570540
10.9%
o 536833
10.3%
t 376309
 
7.2%
e 375238
 
7.2%
r 357373
 
6.9%
N 196849
 
3.8%
h 196849
 
3.8%
E 195778
 
3.8%
Other values (4) 732611
14.0%

Guest_Popularity_percentage
Real number (ℝ)

Missing 

Distinct10019
Distinct (%)1.7%
Missing146030
Missing (%)19.5%
Infinite0
Infinite (%)0.0%
Mean52.236449
Minimum0
Maximum119.91
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2025-04-24T19:07:34.084919image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.79
Q128.38
median53.58
Q376.6
95-th percentile95.1
Maximum119.91
Range119.91
Interquartile range (IQR)48.22

Descriptive statistics

Standard deviation28.451241
Coefficient of variation (CV)0.54466263
Kurtosis-1.1501171
Mean52.236449
Median Absolute Deviation (MAD)24.23
Skewness-0.10703539
Sum31549248
Variance809.47314
MonotonicityNot monotonic
2025-04-24T19:07:34.176841image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.53 378
 
0.1%
29.7 339
 
< 0.1%
42.69 332
 
< 0.1%
54.59 300
 
< 0.1%
41.29 298
 
< 0.1%
71.4 296
 
< 0.1%
84.57 285
 
< 0.1%
65.16 284
 
< 0.1%
70.99 283
 
< 0.1%
69.72 281
 
< 0.1%
Other values (10009) 600894
80.1%
(Missing) 146030
 
19.5%
ValueCountFrequency (%)
0 3
 
< 0.1%
0.01 47
< 0.1%
0.02 13
 
< 0.1%
0.03 27
 
< 0.1%
0.04 88
< 0.1%
0.05 12
 
< 0.1%
0.06 82
< 0.1%
0.07 86
< 0.1%
0.08 16
 
< 0.1%
0.09 51
< 0.1%
ValueCountFrequency (%)
119.91 1
< 0.1%
115.62 2
< 0.1%
115.43 1
< 0.1%
115.41 1
< 0.1%
114.88 1
< 0.1%
114.72 2
< 0.1%
110.14 1
< 0.1%
107.81 2
< 0.1%
107.58 1
< 0.1%
107.34 1
< 0.1%

Number_of_Ads
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.3488549
Minimum0
Maximum103.91
Zeros217592
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2025-04-24T19:07:34.259433image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum103.91
Range103.91
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1511304
Coefficient of variation (CV)0.85341306
Kurtosis505.89391
Mean1.3488549
Median Absolute Deviation (MAD)1
Skewness6.0329918
Sum1011639.8
Variance1.3251012
MonotonicityNot monotonic
2025-04-24T19:07:34.328128image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 217592
29.0%
1 214069
28.5%
3 160173
21.4%
2 158156
21.1%
103.25 2
 
< 0.1%
53.37 1
 
< 0.1%
103.91 1
 
< 0.1%
103 1
 
< 0.1%
53.42 1
 
< 0.1%
103.75 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 217592
29.0%
1 214069
28.5%
2 158156
21.1%
3 160173
21.4%
12 1
 
< 0.1%
53.37 1
 
< 0.1%
53.42 1
 
< 0.1%
103 1
 
< 0.1%
103.25 2
 
< 0.1%
103.75 1
 
< 0.1%
ValueCountFrequency (%)
103.91 1
 
< 0.1%
103.88 1
 
< 0.1%
103.75 1
 
< 0.1%
103.25 2
 
< 0.1%
103 1
 
< 0.1%
53.42 1
 
< 0.1%
53.37 1
 
< 0.1%
12 1
 
< 0.1%
3 160173
21.4%
2 158156
21.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.5 MiB
Neutral
251291 
Negative
250116 
Positive
248593 

Length

Max length8
Median length8
Mean length7.6649453
Min length7

Characters and Unicode

Total characters5748709
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowNegative
3rd rowNegative
4th rowPositive
5th rowNeutral

Common Values

ValueCountFrequency (%)
Neutral 251291
33.5%
Negative 250116
33.3%
Positive 248593
33.1%

Length

2025-04-24T19:07:34.408209image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T19:07:34.472500image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
neutral 251291
33.5%
negative 250116
33.3%
positive 248593
33.1%

Most occurring characters

ValueCountFrequency (%)
e 1000116
17.4%
t 750000
13.0%
i 747302
13.0%
N 501407
8.7%
a 501407
8.7%
v 498709
8.7%
u 251291
 
4.4%
r 251291
 
4.4%
l 251291
 
4.4%
g 250116
 
4.4%
Other values (3) 745779
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5748709
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1000116
17.4%
t 750000
13.0%
i 747302
13.0%
N 501407
8.7%
a 501407
8.7%
v 498709
8.7%
u 251291
 
4.4%
r 251291
 
4.4%
l 251291
 
4.4%
g 250116
 
4.4%
Other values (3) 745779
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5748709
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1000116
17.4%
t 750000
13.0%
i 747302
13.0%
N 501407
8.7%
a 501407
8.7%
v 498709
8.7%
u 251291
 
4.4%
r 251291
 
4.4%
l 251291
 
4.4%
g 250116
 
4.4%
Other values (3) 745779
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5748709
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1000116
17.4%
t 750000
13.0%
i 747302
13.0%
N 501407
8.7%
a 501407
8.7%
v 498709
8.7%
u 251291
 
4.4%
r 251291
 
4.4%
l 251291
 
4.4%
g 250116
 
4.4%
Other values (3) 745779
13.0%

Listening_Time_minutes
Real number (ℝ)

High correlation  Zeros 

Distinct42807
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.437406
Minimum0
Maximum119.97
Zeros8551
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2025-04-24T19:07:34.557691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.07879
Q123.17835
median43.37946
Q364.81158
95-th percentile93.67793
Maximum119.97
Range119.97
Interquartile range (IQR)41.63323

Descriptive statistics

Standard deviation27.138306
Coefficient of variation (CV)0.59726794
Kurtosis-0.66123629
Mean45.437406
Median Absolute Deviation (MAD)20.75602
Skewness0.35081226
Sum34078055
Variance736.48764
MonotonicityNot monotonic
2025-04-24T19:07:34.649389image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8551
 
1.1%
5.82 124
 
< 0.1%
10.55 108
 
< 0.1%
8.75 108
 
< 0.1%
19.71 98
 
< 0.1%
6.16 98
 
< 0.1%
7.92 97
 
< 0.1%
14.93 97
 
< 0.1%
11.91 93
 
< 0.1%
12.78 92
 
< 0.1%
Other values (42797) 740534
98.7%
ValueCountFrequency (%)
0 8551
1.1%
0.00056 7
 
< 0.1%
0.00175 8
 
< 0.1%
0.00661 18
 
< 0.1%
0.0105 7
 
< 0.1%
0.01077 24
 
< 0.1%
0.01257 30
 
< 0.1%
0.0296 15
 
< 0.1%
0.03228 16
 
< 0.1%
0.0343 18
 
< 0.1%
ValueCountFrequency (%)
119.97 22
< 0.1%
119.9 16
< 0.1%
119.8 18
< 0.1%
119.79 14
< 0.1%
119.78 17
< 0.1%
119.74 15
< 0.1%
119.73 14
< 0.1%
119.67 12
< 0.1%
119.66 22
< 0.1%
119.56 17
< 0.1%

Interactions

2025-04-24T19:07:29.641397image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:26.043285image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:26.753829image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:27.517390image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:28.208013image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:28.851858image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:29.786989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:26.168468image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:26.874302image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:27.636008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:28.312553image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:28.968956image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:29.912474image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:26.290648image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:27.072279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:27.747178image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:28.423536image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:29.092897image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:30.019636image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:26.397059image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:27.180329image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:27.859148image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:28.525709image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:29.206287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:30.141403image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:26.527400image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:27.288478image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:27.982176image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:28.634470image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:29.415873image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:30.242153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:26.634063image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:27.397244image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:28.098146image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:28.740539image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-04-24T19:07:29.522827image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2025-04-24T19:07:34.808415image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Episode_Length_minutesEpisode_SentimentGenreGuest_Popularity_percentageHost_Popularity_percentageListening_Time_minutesNumber_of_AdsPodcast_NamePublication_DayPublication_Timeid
Episode_Length_minutes1.0000.0220.014-0.0090.0240.932-0.0580.0310.0150.013-0.001
Episode_Sentiment0.0221.0000.0130.0130.0140.0370.0020.0230.0090.0110.000
Genre0.0140.0131.0000.0140.0130.0160.0020.9980.0140.0110.000
Guest_Popularity_percentage-0.0090.0130.0141.0000.023-0.0140.0090.0190.0110.0110.001
Host_Popularity_percentage0.0240.0140.0130.0231.0000.045-0.0170.0190.0090.0100.000
Listening_Time_minutes0.9320.0370.016-0.0140.0451.000-0.1150.0320.0240.026-0.001
Number_of_Ads-0.0580.0020.0020.009-0.017-0.1151.0000.0050.0010.0010.000
Podcast_Name0.0310.0230.9980.0190.0190.0320.0051.0000.0190.0210.000
Publication_Day0.0150.0090.0140.0110.0090.0240.0010.0191.0000.0090.001
Publication_Time0.0130.0110.0110.0110.0100.0260.0010.0210.0091.0000.000
id-0.0010.0000.0000.0010.000-0.0010.0000.0000.0010.0001.000

Missing values

2025-04-24T19:07:30.425031image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-24T19:07:30.953730image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-24T19:07:31.918152image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idPodcast_NameEpisode_TitleEpisode_Length_minutesGenreHost_Popularity_percentagePublication_DayPublication_TimeGuest_Popularity_percentageNumber_of_AdsEpisode_SentimentListening_Time_minutes
00Mystery MattersEpisode 98NaNTrue Crime74.81ThursdayNightNaN0.0Positive31.41998
11Joke JunctionEpisode 26119.80Comedy66.95SaturdayAfternoon75.952.0Negative88.01241
22Study SessionsEpisode 1673.90Education69.97TuesdayEvening8.970.0Negative44.92531
33Digital DigestEpisode 4567.17Technology57.22MondayMorning78.702.0Positive46.27824
44Mind & BodyEpisode 86110.51Health80.07MondayAfternoon58.683.0Neutral75.61031
55Fitness FirstEpisode 1926.54Health48.96SaturdayAfternoonNaN3.0Positive22.77047
66Criminal MindsEpisode 4769.83True Crime35.82SundayNight39.020.0Neutral64.75024
77News RoundupEpisode 4448.52News44.99ThursdayNight20.120.0Positive22.37517
88Daily DigestEpisode 32105.87News69.81MondayEveningNaN2.0Neutral68.00124
99Music MattersEpisode 81NaNMusic82.18ThursdayNight59.723.0Neutral45.94761
idPodcast_NameEpisode_TitleEpisode_Length_minutesGenreHost_Popularity_percentagePublication_DayPublication_TimeGuest_Popularity_percentageNumber_of_AdsEpisode_SentimentListening_Time_minutes
749990749990Finance FocusEpisode 61114.72Business83.62SundayMorning91.800.0Neutral61.16847
749991749991Business InsightsEpisode 562.46Business30.03TuesdayAfternoonNaN0.0Positive53.32434
749992749992Fashion ForwardEpisode 7548.67Lifestyle88.62WednesdayEvening25.653.0Positive42.08465
749993749993Style GuideEpisode 8323.52Lifestyle38.14TuesdayEvening86.170.0Neutral19.71374
749994749994Laugh LineEpisode 678.93Comedy85.52SaturdayEveningNaN1.0Neutral7.39878
749995749995Learning LabEpisode 2575.66Education69.36SaturdayMorningNaN0.0Negative56.87058
749996749996Business BriefsEpisode 2175.75Business35.21SaturdayNightNaN2.0Neutral45.46242
749997749997Lifestyle LoungeEpisode 5130.98Lifestyle78.58ThursdayMorning84.890.0Negative15.26000
749998749998Style GuideEpisode 47108.98Lifestyle45.39ThursdayMorning93.270.0Negative100.72939
749999749999Sports CentralEpisode 9924.10Sports22.45SaturdayNight36.720.0Neutral11.94439